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Note: Links lead to the DBLP on the Web. Leonard Pitt Joseph Elble , Cinda Heeren , Leonard Pitt: Optimized Disjunctive Association Rules via Sampling. ICDM 2003 : 43-50 Cinda Heeren , H. V. Jagadish , Leonard Pitt: Optimal indexing using near-minimal space. PODS 2003 : 244-251 Nina Mishra , Daniel Oblinger , Leonard Pitt: Sublinear time approximate clustering. SODA 2001 : 439-447 Carlos Domingo , Nina Mishra , Leonard Pitt: Efficient Read-Restricted Monotone CNF/DNF Dualization by Learning with Membership Queries. Machine Learning 37 (1): 89-110 (1999) Stephen Kwek , Leonard Pitt: PAC Learning Intersections of Halfspaces with Membership Queries. Algorithmica 22 (1/2): 53-75 (1998) Howard Aizenstein , Tibor Hegedüs , Lisa Hellerstein , Leonard Pitt: Complexity Theoretic Hardness Results for Query Learning. Computational Complexity 7 (1): 19-53 (1998) Howard Aizenstein , Avrim Blum , Roni Khardon , Eyal Kushilevitz , Leonard Pitt, Dan Roth : On Learning Read-k-Satisfy-j DNF. SIAM J. Comput. 27 (6): 1515-1530 (1998) Haym Hirsh , Nina Mishra , Leonard Pitt: Version Spaces without Boundary Sets. AAAI/IAAI 1997 : 491-496 Leonard Pitt: On Exploiting Knowledge and Concept Use in Learning Theory. ALT 1997 : 62-84 Nina Mishra , Leonard Pitt: Generating all Maximal Independent Sets of Bounded-Degree Hypergraphs. COLT 1997 : 211-217 David P. Helmbold , Stephen Kwek , Leonard Pitt: Learning When to Trust Which Experts. EuroCOLT 1997 : 134-149 Stephen Kwek , Leonard Pitt: PAC Learning Intersections of Halfspaces with Membership Queries (Extended Abstract). COLT 1996 : 244-254 Michael Frazier , Sally A. Goldman , Nina Mishra , Leonard Pitt: Learning from a Consistently Ignorant Teacher. J. Comput. Syst. Sci. 52 (3): 471-492 (1996) Michael Frazier , Leonard Pitt: Classic Learning. Machine Learning 25 (2-3): 151-193 (1996) Howard Aizenstein , Leonard Pitt: On The Learnability Of Disjunctive Normal Form Formulas. Machine Learning 19 (3): 183-208 (1995) Avrim Blum , Roni Khardon , Eyal Kushilevitz , Leonard Pitt, Dan Roth : On Learning Read- k -Satisfy- j DNF. COLT 1994 : 110-117 Michael Frazier , Leonard Pitt: CLASSIC Learning. COLT 1994 : 23-34 Michael Frazier , Sally A. Goldman , Nina Mishra , Leonard Pitt: Learning from a Consistently Ignorant Teacher. COLT 1994 : 328-339 Michael Frazier , Leonard Pitt: Learning From Entailment: An Application to Propositional Horn Sentences. ICML 1993 : 120-127 Leonard Pitt, Manfred K. Warmuth : The Minimum Consistent DFA Problem Cannot be Approximated within any Polynomial. J. ACM 40 (1): 95-142 (1993) Howard Aizenstein , Leonard Pitt: Exact Learning of Read- k Disjoint DNF and Not-So-Disjoint DNF. COLT 1992 : 71-76 Howard Aizenstein , Lisa Hellerstein , Leonard Pitt: Read-Thrice DNF Is Hard to Learn With Membership and Equivalence Queries FOCS 1992 : 523-532 Dan Gusfield , Leonard Pitt: A Bounded Approximation for the Minimum Cost 2-Sat Problem. Algorithmica 8 (2): 103-117 (1992) Dana Angluin , Michael Frazier , Leonard Pitt: Learning Conjunctions of Horn Clauses. Machine Learning 9 : 147-164 (1992) Raymond A. Board , Leonard Pitt: On the Necessity of Occam Algorithms. Theor. Comput. Sci. 100 (1): 157-184 (1992) Robert Daley , Leonard Pitt, Mahendran Velauthapillai , Todd Will : Relations Between Probabilistic and Team One-Shot Learners (Extended Abstract). COLT 1991 : 228-239 Howard Aizenstein , Leonard Pitt: Exact Learning of Read-Twice DNF Formulas (Extended Abstract) FOCS 1991 : 170-179 Dana Angluin , Michael Frazier , Leonard Pitt: Learning Conjunctions of Horn Clauses (Abstract). COLT 1990 : 387 Dana Angluin , Michael Frazier , Leonard Pitt: Learning Conjunctions of Horn Clauses (Extended Abstract) FOCS 1990 : 186-192 Raymond A. Board , Leonard Pitt: On the Necessity of Occam Algorithms STOC 1990 : 54-63 Leonard Pitt, Manfred K. Warmuth : Prediction-Preserving Reducibility. J. Comput. Syst. Sci. 41 (3): 430-467 (1990) Leonard Pitt: Introduction: Special Issue on Computational Learning Theory. Machine Learning 5 : 117-120 (1990) Leonard Pitt: Inductive Inference, DFAs, and Computational Complexity. AII 1989 : 18-44 Michael J. Kearns , Leonard Pitt: A Polynomial-Time Algorithm for Learning k- Variable Pattern Languages from Examples. COLT 1989 : 57-71 Leonard Pitt, Manfred K. Warmuth : The Minimum Consistent DFA Problem Cannot Be Approximated within any Polynomial STOC 1989 : 421-432 Leonard Pitt, Manfred K. Warmuth : The Minimum Consistent DFA Problem Cannot be Approximated within any Polynomial (abstract). Structure in Complexity Theory Conference 1989 : 230 Leonard Pitt: Probabilistic inductive inference. J. ACM 36 (2): 383-433 (1989) Raymond A. Board , Leonard Pitt: Semi-Supervised Learning. Machine Learning 4 : 41-65 (1989) Leonard Pitt, Carl H. Smith : Probability and Plurality for Aggregations of Learning Machines Inf. Comput. 77 (1): 77-92 (1988) Leonard Pitt, Leslie G. Valiant : Computational limitations on learning from examples. J. ACM 35 (4): 965-984 (1988) Leonard Pitt, Carl H. Smith : Probability and Plurality for Aggregations of Learning Machines. ICALP 1987 : 1-10 Michael J. Kearns , Ming Li , Leonard Pitt, Leslie G. Valiant : On the Learnability of Boolean Formulae STOC 1987 : 285-295 Leonard Pitt: A Note on Extending Knuth's Tree Estimator to Directed Acyclic Graphs. Inf. Process. Lett. 24 (3): 203-206 (1987) Leonard Pitt, Robert E. Reinke : Criteria for Polynomial-Time (Conceptual) Clustering. Machine Learning 2 (4): 371-396 (1987) Dan Gusfield , Leonard Pitt: Equivalent Approximation Algorithms for Node Cover. Inf. Process. Lett. 22 (6): 291-294 (1986) Leonard Pitt: A Characterization of Probabilistic Inference FOCS 1984 : 485-494 1 [ 20 ] [ 25 ] [ 26 ] [ 32 ] [ 40 ] [ 41 ] 2 [ 18 ] [ 19 ] [ 23 ] 3 [ 31 ] [ 40 ] 4 [ 9 ] [ 17 ] [ 22 ] 5 [ 21 ] 6 [ 43 ] 7 [ 46 ] 8 [ 18 ] [ 19 ] [ 23 ] [ 28 ] [ 29 ] [ 30 ] [ 33 ] [ 34 ] 9 [ 29 ] [ 34 ] 10 [ 2 ] [ 24 ] 11 [ 45 ] [ 46 ] 12 [ 41 ] 13 [ 25 ] [ 41 ] 14 [ 36 ] 15 [ 39 ] 16 [ 45 ] 17 [ 5 ] [ 13 ] 18 [ 31 ] [ 40 ] 19 [ 31 ] [ 40 ] 20 [ 35 ] [ 36 ] [ 42 ] 21 [ 5 ] 22 [ 29 ] [ 34 ] [ 37 ] [ 39 ] [ 43 ] [ 44 ] 23 [ 44 ] 24 [ 3 ] 25 [ 31 ] [ 40 ] 26 [ 6 ] [ 8 ] 27 [ 5 ] [ 7 ] 28 [ 21 ] 29 [ 11 ] [ 12 ] [ 16 ] [ 27 ] 30 [ 21 ]  ©2004 Association for Computing Machinery |
